import argparse
import logging
import platform
import os
import sys

from copy import deepcopy
from pathlib import Path

# NOTE: old code
# sys.path.append('./')  # to run '$ python *.py' files in subdirectories
# logger = logging.getLogger(__name__)

# NOTE: new code get from YOLOv5
FILE = Path(__file__).resolve()
ROOT = FILE.parents[1]  # YOLOv5 root directory
if str(ROOT) not in sys.path:
    sys.path.append(str(ROOT))  # add ROOT to PATH
if platform.system() != 'Windows':
    ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative

import torch
from models.common import *
from models.experimental import *
from utils.autoanchor import check_anchor_order
from utils.general import make_divisible, check_file, set_logging
from utils.torch_utils import time_synchronized, fuse_conv_and_bn, model_info, scale_img, initialize_weights, \
	select_device, copy_attr
from utils.loss import SigmoidBin

try:
	import thop  # for FLOPS computation
except ImportError:
	thop = None


class Detect(nn.Module):
	stride = None  # strides computed during build
	export = False  # onnx export
	end2end = False
	include_nms = False
	concat = False

	def __init__(self, nc=80, anchors=(), ch=()):  # detection layer
		super(Detect, self).__init__()
		self.nc = nc  # number of classes
		self.no = nc + 5  # number of outputs per anchor
		self.nl = len(anchors)  # number of detection layers
		self.na = len(anchors[0]) // 2  # number of anchors
		self.grid = [torch.zeros(1)] * self.nl  # init grid
		a = torch.tensor(anchors).float().view(self.nl, -1, 2)
		self.register_buffer('anchors', a)  # shape(nl,na,2)
		self.register_buffer('anchor_grid', a.clone().view(self.nl, 1, -1, 1, 1, 2))  # shape(nl,1,na,1,1,2)
		self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch)  # output conv

	def forward(self, x):
		# x = x.copy()  # for profiling
		z = []  # inference output
		self.training |= self.export
		for i in range(self.nl):
			x[i] = self.m[i](x[i])  # conv
			bs, _, ny, nx = x[i].shape  # x(bs,255,20,20) to x(bs,3,20,20,85)
			x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()

			if not self.training:  # inference
				if self.grid[i].shape[2:4] != x[i].shape[2:4]:
					self.grid[i] = self._make_grid(nx, ny).to(x[i].device)
				y = x[i].sigmoid()
				if not torch.onnx.is_in_onnx_export():
					y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i]  # xy
					y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh
				else:
					xy, wh, conf = y.split((2, 2, self.nc + 1), 4)  # y.tensor_split((2, 4, 5), 4)  # torch 1.8.0
					xy = xy * (2. * self.stride[i]) + (self.stride[i] * (self.grid[i] - 0.5))  # new xy
					wh = wh ** 2 * (4 * self.anchor_grid[i].data)  # new wh
					y = torch.cat((xy, wh, conf), 4)
				z.append(y.view(bs, -1, self.no))

		if self.training:
			out = x
		elif self.end2end:
			out = torch.cat(z, 1)
		elif self.include_nms:
			z = self.convert(z)
			out = (z, )
		elif self.concat:
			out = torch.cat(z, 1)
		else:
			out = (torch.cat(z, 1), x)

		return out

	@staticmethod
	def _make_grid(nx=20, ny=20):
		yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
		return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()

	def convert(self, z):
		z = torch.cat(z, 1)
		box = z[:, :, :4]
		conf = z[:, :, 4:5]
		score = z[:, :, 5:]
		score *= conf
		convert_matrix = torch.tensor([[1, 0, 1, 0], [0, 1, 0, 1], [-0.5, 0, 0.5, 0], [0, -0.5, 0, 0.5]],
									  dtype=torch.float32,
									  device=z.device)
		box @= convert_matrix
		return (box, score)


class IDetect(nn.Module):
	stride = None  # strides computed during build
	export = False  # onnx export
	end2end = False
	include_nms = False
	concat = False

	def __init__(self, nc=80, anchors=(), ch=()):  # detection layer
		super(IDetect, self).__init__()
		self.nc = nc  # number of classes
		self.no = nc + 5  # number of outputs per anchor
		self.nl = len(anchors)  # number of detection layers
		self.na = len(anchors[0]) // 2  # number of anchors
		self.grid = [torch.zeros(1)] * self.nl  # init grid
		a = torch.tensor(anchors).float().view(self.nl, -1, 2)
		self.register_buffer('anchors', a)  # shape(nl,na,2)
		self.register_buffer('anchor_grid', a.clone().view(self.nl, 1, -1, 1, 1, 2))  # shape(nl,1,na,1,1,2)
		self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch)  # output conv

		self.ia = nn.ModuleList(ImplicitA(x) for x in ch)
		self.im = nn.ModuleList(ImplicitM(self.no * self.na) for _ in ch)

	def forward(self, x):
		# x = x.copy()  # for profiling
		z = []  # inference output
		self.training |= self.export
		for i in range(self.nl):
			x[i] = self.m[i](self.ia[i](x[i]))  # conv
			x[i] = self.im[i](x[i])
			bs, _, ny, nx = x[i].shape  # x(bs,255,20,20) to x(bs,3,20,20,85)
			x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()

			if not self.training:  # inference
				if self.grid[i].shape[2:4] != x[i].shape[2:4]:
					self.grid[i] = self._make_grid(nx, ny).to(x[i].device)

				y = x[i].sigmoid()
				y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i]  # xy
				y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh
				z.append(y.view(bs, -1, self.no))

		return x if self.training else (torch.cat(z, 1), x)

	def fuseforward(self, x):
		# x = x.copy()  # for profiling
		z = []  # inference output
		self.training |= self.export
		for i in range(self.nl):
			x[i] = self.m[i](x[i])  # conv
			bs, _, ny, nx = x[i].shape  # x(bs,255,20,20) to x(bs,3,20,20,85)
			x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()

			if not self.training:  # inference
				if self.grid[i].shape[2:4] != x[i].shape[2:4]:
					self.grid[i] = self._make_grid(nx, ny).to(x[i].device)

				y = x[i].sigmoid()
				if not torch.onnx.is_in_onnx_export():
					y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i]  # xy
					y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh
				else:
					xy, wh, conf = y.split((2, 2, self.nc + 1), 4)  # y.tensor_split((2, 4, 5), 4)  # torch 1.8.0
					xy = xy * (2. * self.stride[i]) + (self.stride[i] * (self.grid[i] - 0.5))  # new xy
					wh = wh ** 2 * (4 * self.anchor_grid[i].data)  # new wh
					y = torch.cat((xy, wh, conf), 4)
				z.append(y.view(bs, -1, self.no))

		if self.training:
			out = x
		elif self.end2end:
			out = torch.cat(z, 1)
		elif self.include_nms:
			z = self.convert(z)
			out = (z, )
		elif self.concat:
			out = torch.cat(z, 1)
		else:
			out = (torch.cat(z, 1), x)

		return out

	def fuse(self):
		print("IDetect.fuse")
		# fuse ImplicitA and Convolution
		for i in range(len(self.m)):
			c1,c2,_,_ = self.m[i].weight.shape
			c1_,c2_, _,_ = self.ia[i].implicit.shape
			self.m[i].bias += torch.matmul(self.m[i].weight.reshape(c1,c2),self.ia[i].implicit.reshape(c2_,c1_)).squeeze(1)

		# fuse ImplicitM and Convolution
		for i in range(len(self.m)):
			c1,c2, _,_ = self.im[i].implicit.shape
			self.m[i].bias *= self.im[i].implicit.reshape(c2)
			self.m[i].weight *= self.im[i].implicit.transpose(0,1)

	@staticmethod
	def _make_grid(nx=20, ny=20):
		yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
		return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()

	def convert(self, z):
		z = torch.cat(z, 1)
		box = z[:, :, :4]
		conf = z[:, :, 4:5]
		score = z[:, :, 5:]
		score *= conf
		convert_matrix = torch.tensor([[1, 0, 1, 0], [0, 1, 0, 1], [-0.5, 0, 0.5, 0], [0, -0.5, 0, 0.5]],
									  dtype=torch.float32,
									  device=z.device)
		box @= convert_matrix
		return (box, score)


class IKeypoint(nn.Module):
	stride = None  # strides computed during build
	export = False  # onnx export

	def __init__(self, nc=80, anchors=(), nkpt=17, ch=(), inplace=True, dw_conv_kpt=False):  # detection layer
		super(IKeypoint, self).__init__()
		self.nc = nc  # number of classes
		self.nkpt = nkpt
		self.dw_conv_kpt = dw_conv_kpt
		self.no_det=(nc + 5)  # number of outputs per anchor for box and class
		self.no_kpt = 3*self.nkpt ## number of outputs per anchor for keypoints
		self.no = self.no_det+self.no_kpt
		self.nl = len(anchors)  # number of detection layers
		self.na = len(anchors[0]) // 2  # number of anchors
		self.grid = [torch.zeros(1)] * self.nl  # init grid
		self.flip_test = False
		a = torch.tensor(anchors).float().view(self.nl, -1, 2)
		self.register_buffer('anchors', a)  # shape(nl,na,2)
		self.register_buffer('anchor_grid', a.clone().view(self.nl, 1, -1, 1, 1, 2))  # shape(nl,1,na,1,1,2)
		self.m = nn.ModuleList(nn.Conv2d(x, self.no_det * self.na, 1) for x in ch)  # output conv

		self.ia = nn.ModuleList(ImplicitA(x) for x in ch)
		self.im = nn.ModuleList(ImplicitM(self.no_det * self.na) for _ in ch)

		if self.nkpt is not None:
			if self.dw_conv_kpt: #keypoint head is slightly more complex
				self.m_kpt = nn.ModuleList(
					nn.Sequential(DWConv(x, x, k=3), Conv(x,x),
								  DWConv(x, x, k=3), Conv(x, x),
								  DWConv(x, x, k=3), Conv(x,x),
								  DWConv(x, x, k=3), Conv(x, x),
								  DWConv(x, x, k=3), Conv(x, x),
								  DWConv(x, x, k=3), nn.Conv2d(x, self.no_kpt * self.na, 1)) for x in ch)
			else: #keypoint head is a single convolution
				self.m_kpt = nn.ModuleList(nn.Conv2d(x, self.no_kpt * self.na, 1) for x in ch)

		self.inplace = inplace  # use in-place ops (e.g. slice assignment)

	def forward(self, x):
		# x = x.copy()  # for profiling
		z = []  # inference output
		self.training |= self.export
		for i in range(self.nl):
			if self.nkpt is None or self.nkpt==0:
				x[i] = self.im[i](self.m[i](self.ia[i](x[i])))  # conv
			else :
				x[i] = torch.cat((self.im[i](self.m[i](self.ia[i](x[i]))), self.m_kpt[i](x[i])), axis=1)

			bs, _, ny, nx = x[i].shape  # x(bs,255,20,20) to x(bs,3,20,20,85)
			x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
			x_det = x[i][..., :6]
			x_kpt = x[i][..., 6:]

			if not self.training:  # inference
				if self.grid[i].shape[2:4] != x[i].shape[2:4]:
					self.grid[i] = self._make_grid(nx, ny).to(x[i].device)
				kpt_grid_x = self.grid[i][..., 0:1]
				kpt_grid_y = self.grid[i][..., 1:2]

				if self.nkpt == 0:
					y = x[i].sigmoid()
				else:
					y = x_det.sigmoid()

				if self.inplace:
					xy = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i]  # xy
					wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i].view(1, self.na, 1, 1, 2) # wh
					if self.nkpt != 0:
						x_kpt[..., 0::3] = (x_kpt[..., ::3] * 2. - 0.5 + kpt_grid_x.repeat(1,1,1,1,17)) * self.stride[i]  # xy
						x_kpt[..., 1::3] = (x_kpt[..., 1::3] * 2. - 0.5 + kpt_grid_y.repeat(1,1,1,1,17)) * self.stride[i]  # xy
						#x_kpt[..., 0::3] = (x_kpt[..., ::3] + kpt_grid_x.repeat(1,1,1,1,17)) * self.stride[i]  # xy
						#x_kpt[..., 1::3] = (x_kpt[..., 1::3] + kpt_grid_y.repeat(1,1,1,1,17)) * self.stride[i]  # xy
						#print('=============')
						#print(self.anchor_grid[i].shape)
						#print(self.anchor_grid[i][...,0].unsqueeze(4).shape)
						#print(x_kpt[..., 0::3].shape)
						#x_kpt[..., 0::3] = ((x_kpt[..., 0::3].tanh() * 2.) ** 3 * self.anchor_grid[i][...,0].unsqueeze(4).repeat(1,1,1,1,self.nkpt)) + kpt_grid_x.repeat(1,1,1,1,17) * self.stride[i]  # xy
						#x_kpt[..., 1::3] = ((x_kpt[..., 1::3].tanh() * 2.) ** 3 * self.anchor_grid[i][...,1].unsqueeze(4).repeat(1,1,1,1,self.nkpt)) + kpt_grid_y.repeat(1,1,1,1,17) * self.stride[i]  # xy
						#x_kpt[..., 0::3] = (((x_kpt[..., 0::3].sigmoid() * 4.) ** 2 - 8.) * self.anchor_grid[i][...,0].unsqueeze(4).repeat(1,1,1,1,self.nkpt)) + kpt_grid_x.repeat(1,1,1,1,17) * self.stride[i]  # xy
						#x_kpt[..., 1::3] = (((x_kpt[..., 1::3].sigmoid() * 4.) ** 2 - 8.) * self.anchor_grid[i][...,1].unsqueeze(4).repeat(1,1,1,1,self.nkpt)) + kpt_grid_y.repeat(1,1,1,1,17) * self.stride[i]  # xy
						x_kpt[..., 2::3] = x_kpt[..., 2::3].sigmoid()

					y = torch.cat((xy, wh, y[..., 4:], x_kpt), dim = -1)

				else:  # for YOLOv5 on AWS Inferentia https://github.com/ultralytics/yolov5/pull/2953
					xy = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i]  # xy
					wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh
					if self.nkpt != 0:
						y[..., 6:] = (y[..., 6:] * 2. - 0.5 + self.grid[i].repeat((1,1,1,1,self.nkpt))) * self.stride[i]  # xy
					y = torch.cat((xy, wh, y[..., 4:]), -1)

				z.append(y.view(bs, -1, self.no))

		return x if self.training else (torch.cat(z, 1), x)

	@staticmethod
	def _make_grid(nx=20, ny=20):
		yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
		return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()


class IAuxDetect(nn.Module):
	stride = None  # strides computed during build
	export = False  # onnx export
	end2end = False
	include_nms = False
	concat = False

	def __init__(self, nc=80, anchors=(), ch=()):  # detection layer
		super(IAuxDetect, self).__init__()
		self.nc = nc  # number of classes
		self.no = nc + 5  # number of outputs per anchor
		self.nl = len(anchors)  # number of detection layers
		self.na = len(anchors[0]) // 2  # number of anchors
		self.grid = [torch.zeros(1)] * self.nl  # init grid
		a = torch.tensor(anchors).float().view(self.nl, -1, 2)
		self.register_buffer('anchors', a)  # shape(nl,na,2)
		self.register_buffer('anchor_grid', a.clone().view(self.nl, 1, -1, 1, 1, 2))  # shape(nl,1,na,1,1,2)
		self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch[:self.nl])  # output conv
		self.m2 = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch[self.nl:])  # output conv

		self.ia = nn.ModuleList(ImplicitA(x) for x in ch[:self.nl])
		self.im = nn.ModuleList(ImplicitM(self.no * self.na) for _ in ch[:self.nl])

	def forward(self, x):
		# x = x.copy()  # for profiling
		z = []  # inference output
		self.training |= self.export
		for i in range(self.nl):
			x[i] = self.m[i](self.ia[i](x[i]))  # conv
			x[i] = self.im[i](x[i])
			bs, _, ny, nx = x[i].shape  # x(bs,255,20,20) to x(bs,3,20,20,85)
			x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()

			x[i+self.nl] = self.m2[i](x[i+self.nl])
			x[i+self.nl] = x[i+self.nl].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()

			if not self.training:  # inference
				if self.grid[i].shape[2:4] != x[i].shape[2:4]:
					self.grid[i] = self._make_grid(nx, ny).to(x[i].device)

				y = x[i].sigmoid()
				if not torch.onnx.is_in_onnx_export():
					y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i]  # xy
					y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh
				else:
					xy, wh, conf = y.split((2, 2, self.nc + 1), 4)  # y.tensor_split((2, 4, 5), 4)  # torch 1.8.0
					xy = xy * (2. * self.stride[i]) + (self.stride[i] * (self.grid[i] - 0.5))  # new xy
					wh = wh ** 2 * (4 * self.anchor_grid[i].data)  # new wh
					y = torch.cat((xy, wh, conf), 4)
				z.append(y.view(bs, -1, self.no))

		return x if self.training else (torch.cat(z, 1), x[:self.nl])

	def fuseforward(self, x):
		# x = x.copy()  # for profiling
		z = []  # inference output
		self.training |= self.export
		for i in range(self.nl):
			x[i] = self.m[i](x[i])  # conv
			bs, _, ny, nx = x[i].shape  # x(bs,255,20,20) to x(bs,3,20,20,85)
			x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()

			if not self.training:  # inference
				if self.grid[i].shape[2:4] != x[i].shape[2:4]:
					self.grid[i] = self._make_grid(nx, ny).to(x[i].device)

				y = x[i].sigmoid()
				if not torch.onnx.is_in_onnx_export():
					y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i]  # xy
					y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh
				else:
					xy = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i]  # xy
					wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i].data  # wh
					y = torch.cat((xy, wh, y[..., 4:]), -1)
				z.append(y.view(bs, -1, self.no))

		if self.training:
			out = x
		elif self.end2end:
			out = torch.cat(z, 1)
		elif self.include_nms:
			z = self.convert(z)
			out = (z, )
		elif self.concat:
			out = torch.cat(z, 1)
		else:
			out = (torch.cat(z, 1), x)

		return out

	def fuse(self):
		print("IAuxDetect.fuse")
		# fuse ImplicitA and Convolution
		for i in range(len(self.m)):
			c1,c2,_,_ = self.m[i].weight.shape
			c1_,c2_, _,_ = self.ia[i].implicit.shape
			self.m[i].bias += torch.matmul(self.m[i].weight.reshape(c1,c2),self.ia[i].implicit.reshape(c2_,c1_)).squeeze(1)

		# fuse ImplicitM and Convolution
		for i in range(len(self.m)):
			c1,c2, _,_ = self.im[i].implicit.shape
			self.m[i].bias *= self.im[i].implicit.reshape(c2)
			self.m[i].weight *= self.im[i].implicit.transpose(0,1)

	@staticmethod
	def _make_grid(nx=20, ny=20):
		yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
		return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()

	def convert(self, z):
		z = torch.cat(z, 1)
		box = z[:, :, :4]
		conf = z[:, :, 4:5]
		score = z[:, :, 5:]
		score *= conf
		convert_matrix = torch.tensor([[1, 0, 1, 0], [0, 1, 0, 1], [-0.5, 0, 0.5, 0], [0, -0.5, 0, 0.5]],
									  dtype=torch.float32,
									  device=z.device)
		box @= convert_matrix
		return (box, score)


class IBin(nn.Module):
	stride = None  # strides computed during build
	export = False  # onnx export

	def __init__(self, nc=80, anchors=(), ch=(), bin_count=21):  # detection layer
		super(IBin, self).__init__()
		self.nc = nc  # number of classes
		self.bin_count = bin_count

		self.w_bin_sigmoid = SigmoidBin(bin_count=self.bin_count, min=0.0, max=4.0)
		self.h_bin_sigmoid = SigmoidBin(bin_count=self.bin_count, min=0.0, max=4.0)
		# classes, x,y,obj
		self.no = nc + 3 + \
				  self.w_bin_sigmoid.get_length() + self.h_bin_sigmoid.get_length()   # w-bce, h-bce
		# + self.x_bin_sigmoid.get_length() + self.y_bin_sigmoid.get_length()

		self.nl = len(anchors)  # number of detection layers
		self.na = len(anchors[0]) // 2  # number of anchors
		self.grid = [torch.zeros(1)] * self.nl  # init grid
		a = torch.tensor(anchors).float().view(self.nl, -1, 2)
		self.register_buffer('anchors', a)  # shape(nl,na,2)
		self.register_buffer('anchor_grid', a.clone().view(self.nl, 1, -1, 1, 1, 2))  # shape(nl,1,na,1,1,2)
		self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch)  # output conv

		self.ia = nn.ModuleList(ImplicitA(x) for x in ch)
		self.im = nn.ModuleList(ImplicitM(self.no * self.na) for _ in ch)

	def forward(self, x):

		#self.x_bin_sigmoid.use_fw_regression = True
		#self.y_bin_sigmoid.use_fw_regression = True
		self.w_bin_sigmoid.use_fw_regression = True
		self.h_bin_sigmoid.use_fw_regression = True

		# x = x.copy()  # for profiling
		z = []  # inference output
		self.training |= self.export
		for i in range(self.nl):
			x[i] = self.m[i](self.ia[i](x[i]))  # conv
			x[i] = self.im[i](x[i])
			bs, _, ny, nx = x[i].shape  # x(bs,255,20,20) to x(bs,3,20,20,85)
			x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()

			if not self.training:  # inference
				if self.grid[i].shape[2:4] != x[i].shape[2:4]:
					self.grid[i] = self._make_grid(nx, ny).to(x[i].device)

				y = x[i].sigmoid()
				y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i]  # xy
				#y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh


				#px = (self.x_bin_sigmoid.forward(y[..., 0:12]) + self.grid[i][..., 0]) * self.stride[i]
				#py = (self.y_bin_sigmoid.forward(y[..., 12:24]) + self.grid[i][..., 1]) * self.stride[i]

				pw = self.w_bin_sigmoid.forward(y[..., 2:24]) * self.anchor_grid[i][..., 0]
				ph = self.h_bin_sigmoid.forward(y[..., 24:46]) * self.anchor_grid[i][..., 1]

				#y[..., 0] = px
				#y[..., 1] = py
				y[..., 2] = pw
				y[..., 3] = ph

				y = torch.cat((y[..., 0:4], y[..., 46:]), dim=-1)

				z.append(y.view(bs, -1, y.shape[-1]))

		return x if self.training else (torch.cat(z, 1), x)

	@staticmethod
	def _make_grid(nx=20, ny=20):
		yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
		return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()


class Model(nn.Module):
	def __init__(self, cfg='yolor-csp-c.yaml', ch=3, nc=None, anchors=None):  # model, input channels, number of classes
		super(Model, self).__init__()
		self.traced = False
		if isinstance(cfg, dict):
			self.yaml = cfg  # model dict
		else:  # is *.yaml
			import yaml  # for torch hub
			self.yaml_file = Path(cfg).name
			with open(cfg) as f:
				self.yaml = yaml.load(f, Loader=yaml.SafeLoader)  # model dict

		# Define model
		ch = self.yaml['ch'] = self.yaml.get('ch', ch)  # input channels
		if nc and nc != self.yaml['nc']:
			logger.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")
			self.yaml['nc'] = nc  # override yaml value
		if anchors:
			logger.info(f'Overriding model.yaml anchors with anchors={anchors}')
			self.yaml['anchors'] = round(anchors)  # override yaml value
		self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch])  # model, savelist
		self.names = [str(i) for i in range(self.yaml['nc'])]  # default names
		# print([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))])

		# Build strides, anchors
		m = self.model[-1]  # Detect()
		if isinstance(m, Detect):
			s = 256  # 2x min stride
			m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))])  # forward
			check_anchor_order(m)
			m.anchors /= m.stride.view(-1, 1, 1)
			self.stride = m.stride
			self._initialize_biases()  # only run once
			# print('Strides: %s' % m.stride.tolist())
		if isinstance(m, IDetect):
			s = 256  # 2x min stride
			m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))])  # forward
			check_anchor_order(m)
			m.anchors /= m.stride.view(-1, 1, 1)
			self.stride = m.stride
			self._initialize_biases()  # only run once
			# print('Strides: %s' % m.stride.tolist())
		if isinstance(m, IAuxDetect):
			s = 256  # 2x min stride
			m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))[:4]])  # forward
			#print(m.stride)
			check_anchor_order(m)
			m.anchors /= m.stride.view(-1, 1, 1)
			self.stride = m.stride
			self._initialize_aux_biases()  # only run once
			# print('Strides: %s' % m.stride.tolist())
		if isinstance(m, IBin):
			s = 256  # 2x min stride
			m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))])  # forward
			check_anchor_order(m)
			m.anchors /= m.stride.view(-1, 1, 1)
			self.stride = m.stride
			self._initialize_biases_bin()  # only run once
			# print('Strides: %s' % m.stride.tolist())
		if isinstance(m, IKeypoint):
			s = 256  # 2x min stride
			m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))])  # forward
			check_anchor_order(m)
			m.anchors /= m.stride.view(-1, 1, 1)
			self.stride = m.stride
			self._initialize_biases_kpt()  # only run once
			# print('Strides: %s' % m.stride.tolist())

		# Init weights, biases
		initialize_weights(self)
		self.info()
		logger.info('')

	def forward(self, x, augment=False, profile=False):
		if augment:
			img_size = x.shape[-2:]  # height, width
			s = [1, 0.83, 0.67]  # scales
			f = [None, 3, None]  # flips (2-ud, 3-lr)
			y = []  # outputs
			for si, fi in zip(s, f):
				xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max()))
				yi = self.forward_once(xi)[0]  # forward
				# cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1])  # save
				yi[..., :4] /= si  # de-scale
				if fi == 2:
					yi[..., 1] = img_size[0] - yi[..., 1]  # de-flip ud
				elif fi == 3:
					yi[..., 0] = img_size[1] - yi[..., 0]  # de-flip lr
				y.append(yi)
			return torch.cat(y, 1), None  # augmented inference, train
		else:
			return self.forward_once(x, profile)  # single-scale inference, train

	def forward_once(self, x, profile=False):
		y, dt = [], []  # outputs
		for m in self.model:
			if m.f != -1:  # if not from previous layer
				x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layers

			if not hasattr(self, 'traced'):
				self.traced=False

			if self.traced:
				if isinstance(m, Detect) or isinstance(m, IDetect) or isinstance(m, IAuxDetect) or isinstance(m, IKeypoint):
					break

			if profile:
				c = isinstance(m, (Detect, IDetect, IAuxDetect, IBin))
				o = thop.profile(m, inputs=(x.copy() if c else x,), verbose=False)[0] / 1E9 * 2 if thop else 0  # FLOPS
				for _ in range(10):
					m(x.copy() if c else x)
				t = time_synchronized()
				for _ in range(10):
					m(x.copy() if c else x)
				dt.append((time_synchronized() - t) * 100)
				print('%10.1f%10.0f%10.1fms %-40s' % (o, m.np, dt[-1], m.type))

			x = m(x)  # run

			y.append(x if m.i in self.save else None)  # save output

		if profile:
			print('%.1fms total' % sum(dt))
		return x

	def _initialize_biases(self, cf=None):  # initialize biases into Detect(), cf is class frequency
		# https://arxiv.org/abs/1708.02002 section 3.3
		# cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1.
		m = self.model[-1]  # Detect() module
		for mi, s in zip(m.m, m.stride):  # from
			b = mi.bias.view(m.na, -1)  # conv.bias(255) to (3,85)
			b.data[:, 4] += math.log(8 / (640 / s) ** 2)  # obj (8 objects per 640 image)
			b.data[:, 5:] += math.log(0.6 / (m.nc - 0.99)) if cf is None else torch.log(cf / cf.sum())  # cls
			mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)

	def _initialize_aux_biases(self, cf=None):  # initialize biases into Detect(), cf is class frequency
		# https://arxiv.org/abs/1708.02002 section 3.3
		# cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1.
		m = self.model[-1]  # Detect() module
		for mi, mi2, s in zip(m.m, m.m2, m.stride):  # from
			b = mi.bias.view(m.na, -1)  # conv.bias(255) to (3,85)
			b.data[:, 4] += math.log(8 / (640 / s) ** 2)  # obj (8 objects per 640 image)
			b.data[:, 5:] += math.log(0.6 / (m.nc - 0.99)) if cf is None else torch.log(cf / cf.sum())  # cls
			mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
			b2 = mi2.bias.view(m.na, -1)  # conv.bias(255) to (3,85)
			b2.data[:, 4] += math.log(8 / (640 / s) ** 2)  # obj (8 objects per 640 image)
			b2.data[:, 5:] += math.log(0.6 / (m.nc - 0.99)) if cf is None else torch.log(cf / cf.sum())  # cls
			mi2.bias = torch.nn.Parameter(b2.view(-1), requires_grad=True)

	def _initialize_biases_bin(self, cf=None):  # initialize biases into Detect(), cf is class frequency
		# https://arxiv.org/abs/1708.02002 section 3.3
		# cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1.
		m = self.model[-1]  # Bin() module
		bc = m.bin_count
		for mi, s in zip(m.m, m.stride):  # from
			b = mi.bias.view(m.na, -1)  # conv.bias(255) to (3,85)
			old = b[:, (0,1,2,bc+3)].data
			obj_idx = 2*bc+4
			b[:, :obj_idx].data += math.log(0.6 / (bc + 1 - 0.99))
			b[:, obj_idx].data += math.log(8 / (640 / s) ** 2)  # obj (8 objects per 640 image)
			b[:, (obj_idx+1):].data += math.log(0.6 / (m.nc - 0.99)) if cf is None else torch.log(cf / cf.sum())  # cls
			b[:, (0,1,2,bc+3)].data = old
			mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)

	def _initialize_biases_kpt(self, cf=None):  # initialize biases into Detect(), cf is class frequency
		# https://arxiv.org/abs/1708.02002 section 3.3
		# cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1.
		m = self.model[-1]  # Detect() module
		for mi, s in zip(m.m, m.stride):  # from
			b = mi.bias.view(m.na, -1)  # conv.bias(255) to (3,85)
			b.data[:, 4] += math.log(8 / (640 / s) ** 2)  # obj (8 objects per 640 image)
			b.data[:, 5:] += math.log(0.6 / (m.nc - 0.99)) if cf is None else torch.log(cf / cf.sum())  # cls
			mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)

	def _print_biases(self):
		m = self.model[-1]  # Detect() module
		for mi in m.m:  # from
			b = mi.bias.detach().view(m.na, -1).T  # conv.bias(255) to (3,85)
			print(('%6g Conv2d.bias:' + '%10.3g' * 6) % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean()))

	# def _print_weights(self):
	#     for m in self.model.modules():
	#         if type(m) is Bottleneck:
	#             print('%10.3g' % (m.w.detach().sigmoid() * 2))  # shortcut weights

	def fuse(self):  # fuse model Conv2d() + BatchNorm2d() layers
		print('Fusing layers... ')
		for m in self.model.modules():
			if isinstance(m, RepConv):
				#print(f" fuse_repvgg_block")
				m.fuse_repvgg_block()
			elif isinstance(m, RepConv_OREPA):
				#print(f" switch_to_deploy")
				m.switch_to_deploy()
			elif type(m) is Conv and hasattr(m, 'bn'):
				m.conv = fuse_conv_and_bn(m.conv, m.bn)  # update conv
				delattr(m, 'bn')  # remove batchnorm
				m.forward = m.fuseforward  # update forward
			elif isinstance(m, (IDetect, IAuxDetect)):
				m.fuse()
				m.forward = m.fuseforward
		self.info()
		return self

	def nms(self, mode=True):  # add or remove NMS module
		present = type(self.model[-1]) is NMS  # last layer is NMS
		if mode and not present:
			print('Adding NMS... ')
			m = NMS()  # module
			m.f = -1  # from
			m.i = self.model[-1].i + 1  # index
			self.model.add_module(name='%s' % m.i, module=m)  # add
			self.eval()
		elif not mode and present:
			print('Removing NMS... ')
			self.model = self.model[:-1]  # remove
		return self

	def autoshape(self):  # add autoShape module
		print('Adding autoShape... ')
		m = autoShape(self)  # wrap model
		copy_attr(m, self, include=('yaml', 'nc', 'hyp', 'names', 'stride'), exclude=())  # copy attributes
		return m

	def info(self, verbose=False, img_size=640):  # print model information
		model_info(self, verbose, img_size)


def parse_model(d, ch):  # model_dict, input_channels(3)
	logger.info('\n%3s%18s%3s%10s  %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments'))
	anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple']
	na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors  # number of anchors
	no = na * (nc + 5)  # number of outputs = anchors * (classes + 5)

	layers, save, c2 = [], [], ch[-1]  # layers, savelist, ch out
	for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']):  # from, number, module, args
		m = eval(m) if isinstance(m, str) else m  # eval strings
		for j, a in enumerate(args):
			try:
				args[j] = eval(a) if isinstance(a, str) else a  # eval strings
			except:
				pass

		n = max(round(n * gd), 1) if n > 1 else n  # depth gain
		if m in [nn.Conv2d, Conv, RobustConv, RobustConv2, DWConv, GhostConv, RepConv, RepConv_OREPA, DownC,
				 SPP, SPPF, SPPCSPC, GhostSPPCSPC, MixConv2d, Focus, Stem, GhostStem, CrossConv,
				 Bottleneck, BottleneckCSPA, BottleneckCSPB, BottleneckCSPC,
				 RepBottleneck, RepBottleneckCSPA, RepBottleneckCSPB, RepBottleneckCSPC,
				 Res, ResCSPA, ResCSPB, ResCSPC,
				 RepRes, RepResCSPA, RepResCSPB, RepResCSPC,
				 ResX, ResXCSPA, ResXCSPB, ResXCSPC,
				 RepResX, RepResXCSPA, RepResXCSPB, RepResXCSPC,
				 Ghost, GhostCSPA, GhostCSPB, GhostCSPC,
				 SwinTransformerBlock, STCSPA, STCSPB, STCSPC,
				 SwinTransformer2Block, ST2CSPA, ST2CSPB, ST2CSPC]:
			c1, c2 = ch[f], args[0]
			if c2 != no:  # if not output
				c2 = make_divisible(c2 * gw, 8)

			args = [c1, c2, *args[1:]]
			if m in [DownC, SPPCSPC, GhostSPPCSPC,
					 BottleneckCSPA, BottleneckCSPB, BottleneckCSPC,
					 RepBottleneckCSPA, RepBottleneckCSPB, RepBottleneckCSPC,
					 ResCSPA, ResCSPB, ResCSPC,
					 RepResCSPA, RepResCSPB, RepResCSPC,
					 ResXCSPA, ResXCSPB, ResXCSPC,
					 RepResXCSPA, RepResXCSPB, RepResXCSPC,
					 GhostCSPA, GhostCSPB, GhostCSPC,
					 STCSPA, STCSPB, STCSPC,
					 ST2CSPA, ST2CSPB, ST2CSPC]:
				args.insert(2, n)  # number of repeats
				n = 1
		elif m is nn.BatchNorm2d:
			args = [ch[f]]
		elif m is Concat:
			c2 = sum([ch[x] for x in f])
		elif m is Chuncat:
			c2 = sum([ch[x] for x in f])
		elif m is Shortcut:
			c2 = ch[f[0]]
		elif m is Foldcut:
			c2 = ch[f] // 2
		elif m in [Detect, IDetect, IAuxDetect, IBin, IKeypoint]:
			args.append([ch[x] for x in f])
			if isinstance(args[1], int):  # number of anchors
				args[1] = [list(range(args[1] * 2))] * len(f)
		elif m is ReOrg:
			c2 = ch[f] * 4
		elif m is Contract:
			c2 = ch[f] * args[0] ** 2
		elif m is Expand:
			c2 = ch[f] // args[0] ** 2
		else:
			c2 = ch[f]

		m_ = nn.Sequential(*[m(*args) for _ in range(n)]) if n > 1 else m(*args)  # module
		t = str(m)[8:-2].replace('__main__.', '')  # module type
		np = sum([x.numel() for x in m_.parameters()])  # number params
		m_.i, m_.f, m_.type, m_.np = i, f, t, np  # attach index, 'from' index, type, number params
		logger.info('%3s%18s%3s%10.0f  %-40s%-30s' % (i, f, n, np, t, args))  # print
		save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1)  # append to savelist
		layers.append(m_)
		if i == 0:
			ch = []
		ch.append(c2)
	return nn.Sequential(*layers), sorted(save)


if __name__ == '__main__':
	parser = argparse.ArgumentParser()
	parser.add_argument('--cfg', type=str, default='yolor-csp-c.yaml', help='model.yaml')
	parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
	parser.add_argument('--profile', action='store_true', help='profile model speed')
	opt = parser.parse_args()
	opt.cfg = check_file(opt.cfg)  # check file
	set_logging()
	device = select_device(opt.device)

	# Create model
	model = Model(opt.cfg).to(device)
	model.train()

	if opt.profile:
		img = torch.rand(1, 3, 640, 640).to(device)
		y = model(img, profile=True)

	# Profile
	# img = torch.rand(8 if torch.cuda.is_available() else 1, 3, 640, 640).to(device)
	# y = model(img, profile=True)

	# Tensorboard
	# from torch.utils.tensorboard import SummaryWriter
	# tb_writer = SummaryWriter()
	# print("Run 'tensorboard --logdir=models/runs' to view tensorboard at http://localhost:6006/")
	# tb_writer.add_graph(model.model, img)  # add model to tensorboard
	# tb_writer.add_image('test', img[0], dataformats='CWH')  # add model to tensorboard
